Latest artificial intelligence provides fast, accurate and consistent detection of multiple sclerosis lesions

Background: Artificial intelligence (AI) algorithms have already had a major impact on medical imaging and opened a wide field of detection of textural and morphological patterns. Our aim was to evaluate the potential of latest AI regarding diagnosis and follow-up of Multiple Sclerosis (MS) in clinical radiology. Methods: We included 101 patients who had undergone MRI at a single academic hospital to evaluate MS lesions according to McDonald criteiia. [T.sub.2w] 3D FLAIR and T1w MPRAGE sequences were processed by two AI (Fig. 1)-deep learning ("latest") and traditional computer vision techniques ("previous")-and analyzed by three expert neuroradiologists (gold standard) independently. Statistical metrics were calculated and compared as follows: Sensitivity (TPR), specificity (TNR), overall accuracy (ACC), false positive rate (FPR) and Dice similarity score (DSC). Results: A comparison of two artificial neuronal networks (ANN) corroborates the superiority of the latest generation AI in detection of MS lesions (Fig. 2). Overall sensitivity (77% vs. 29%) and DSC (0.81 vs 0.39) of the latest version AI were significantly higher as compared to previous version. In the periventricular compartment TPR (77% vs. 53%), ACC (92% vs. 87%) and DSC (0.8 vs 0.64) were higher, while TNR (96% vs. 96%) and FPR (0.043 vs. 0.041) did not change significantly. In the juxtacortical compartment TPR (62% vs. 0.5%), ACC (95% vs. 90%), FPR (0.018 vs. 0.001) and DSC (0.7 vs 0.01) were higher, while TNR (98% vs. 99%) was unaltered. In the deep white matter TPR (82% vs. 46%), ACC (82% vs. 62%) and DSC (0.85 vs 0.6) were higher, while TNR (80% vs. 86%) was lower. FPR (0.14 vs. 0.20) did not change significantly. Infratentorial TPR (53% vs. 16%) and DSC (0.69 vs 0.27) were higher, while TNR (99% vs. 99%), ACC (97% vs. 95%) and FPR (0.015 vs. 0.015) did not change significantly. Conclusion: Preliminary data show that the latest generation AI provides consistent, automated and fully reproducible assessment of MS lesions without being influenced by intra- and/or interobserver intrinsic human variability-especially in the context of longitudinal patient follow-up. Thus, this novel tool may provide improved reliability and standardization in diagnosis and follow-up imaging of MS.